Test paper printer with noisy point removing function

Abstract

The utility model discloses a test paper printer with a noisy point removing function, and relates to the technical field of printers, the test paper printer with the noisy point removing function comprises a printer main body, optical glass is arranged at the top of the printer main body, an image scanning assembly is arranged below the optical glass, and a control system is arranged in the printer main body; the control system is provided with an image acquisition module and an image output module, and the image acquisition module and the image output module are electrically connected with the processor; according to the utility model, when a computer is used for printing test paper, pictures can be transmitted to the image acquisition module, the processor analyzes the pictures and removes noisy points of the pictures, and then the control circuit controls the internal printing assembly to print the test paper. When the test paper is renovated and copied, the image scanning assembly scans the content on the test paper, and then transmits the content to the processor, and the content is printed after noisy point removal processing.

Description

Technical Field

[0001] The utility model specifically relates to the technical field of printers, and more specifically, it is a test paper printer with a function of removing noise points. Background Technique

[0002] A printer is one of the output devices of a computer, used to print the processing results of the computer on relevant media. There are three indicators to measure the quality of a printer: print resolution, print speed, and noise points. There are many types of printers. According to whether the printing element has a striking action on the paper, it is divided into impact printers and non-impact printers. According to the printing character structure, it is divided into full-shape character printers and dot-matrix character printers. According to the way a line of characters is formed on the paper, it is divided into serial printers and line printers. According to the technology adopted, it is divided into cylindrical, spherical, inkjet, thermal, laser, electrostatic, magnetic, light-emitting diode printers, etc.

[0003] Chinese Patent Publication No. CN 212289220 U provides a test paper receiving printer, with an upper shell arranged above the base; a switch button is arranged on the front view surface of the upper shell, and a power interface, a firmware port, and a network interface are arranged at the rear of the base; a printer unit is arranged at the middle position inside the base and the upper shell; a printer paper outlet is arranged on the upper shell above the printer unit; the microcontroller unit is connected to an integrated chip and a TF card storage unit.

[0004] The test paper printer in the above patent only simply prints and cannot realize the functions of copying the test paper and processing the test paper surface. Content of the Utility Model

[0005] The purpose of the utility model is to provide a test paper printer with a function of removing noise points, which can perform noise removal processing on the pictures to be printed or the test papers to be copied, so as to ensure the neatness of the test paper surface and reduce the appearance of mosaic blocks, in order to solve the technical problems raised in the above background technique.

[0006] To achieve the above purpose, the utility model provides the following technical solutions:

[0007] A test paper printer with a function of removing noise points, including

[0008] A printer main body, the top of the printer main body has an optical glass for copying test papers, an image scanning component capable of acquiring test paper content is arranged below the optical glass, and a cover plate is movably connected to the top of the printer main body;

[0009] The interior of the printer main body is provided with a control system; the control system has an image acquisition module and an image output module, both the image acquisition module and the image output module are electrically connected to a processor; the processor is also electrically connected to a control circuit; the image scanning assembly is electrically connected to the processor.

[0010] As a further technical solution of the present utility model, a paper feeding mechanism is cooperatively installed at the front end of the printer main body, and a paper storage box is arranged outside the paper feeding mechanism.

[0011] As a further technical solution of the present utility model, a support rod is fixed at the rear of the printer main body, and a display screen is cooperatively installed at the upper end of the support rod; the display screen is electrically connected to the image output module.

[0012] As a further technical solution of the present utility model, the image scanning assembly has a light source, a scanner, a lens group and an image sensor.

[0013] As a further technical solution of the present utility model, the light source is a light emitting diode or a cold cathode fluorescent lamp.

[0014] Compared with the prior art, the beneficial effects of the present utility model are:

[0015] 1. In the present utility model, when using a computer to print test papers, pictures will be transmitted to the image acquisition module, the processor analyzes the pictures and performs noise removal processing on the pictures, and then controls the internal printing components to perform printing through the control circuit.

[0016] 2. In the present utility model, when renovating and copying test papers, the image scanning assembly scans the content on the test papers and then transmits it to the processor, and after noise removal processing, it is printed.

[0017] 3. In the present utility model, when copying test papers, after being scanned by the image scanning assembly, image information will be formed; after noise removal, the image information of the test papers will be displayed on the display screen, so as to facilitate the operator to view the noise removal effect; if the noise removal is not ideal, deep processing can also be performed. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 is a three-dimensional structural schematic diagram of the present utility model.

[0019] Figure 2 is in the present utility model Figure 1 rear side structural schematic diagram.

[0020] Figure 3 is in the present utility model Figure 1 front view.

[0021] Figure 4 This is the image processing flowchart of the present utility model.

[0022] Figure 5 This is the learning and training block diagram of the present utility model.

[0023] Figure 6 This is the circuit block diagram of the present utility model.

[0024] In the figure: 1 - printer main body, 2 - optical glass, 3 - image scanning component, 4 - cover plate, 5 - paper storage box, 6 - paper feeding mechanism, 7 - support rod, 8 - display screen, 9 - control system;

[0025] 91 - processor, 92 - image acquisition module, 93 - image output module, 94 - control circuit. Specific embodiments

[0026] Next, the technical solutions in the embodiments of the present utility model will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present utility model. Obviously, the described embodiments are only a part of the embodiments of the present utility model, rather than all the embodiments. Based on the embodiments of the present utility model, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of the present utility model.

[0027] Please refer to Figures 1-6 , in the embodiment of the present utility model, a test paper printer with a noise reduction function includes a printer main body 1. The top of the printer main body 1 has an optical glass 2 for test paper copying. Below the optical glass 2, there is an image scanning component 3 capable of obtaining the content of the test paper, and a cover plate 4 is movably connected to the top of the printer main body 1;

[0028] The interior of the printer main body 1 is provided with a control system 9; the control system 9 has an image acquisition module 92 and an image output module 93. Both the image acquisition module 92 and the image output module 93 are electrically connected to the processor 91; the processor 91 is also electrically connected to the control circuit 94; the image scanning component 3 is electrically connected to the processor 91.

[0029] By adopting the above technical solutions, when using a computer to print test papers, the picture will be transmitted to the image acquisition module 92. The processor 91 analyzes the picture and performs noise reduction processing on the picture, and then controls the internal printing component to print through the control circuit 94;

[0030] When renovating and copying test papers, the image scanning component 3 will scan the content on the test paper, then transmit it to the processor 91, and perform printing after noise reduction processing.

[0031] In this embodiment, a paper feeding mechanism 6 is cooperatively installed at the front end of the printer main body 1, and a paper storage box 5 is arranged outside the paper feeding mechanism 6.

[0032] By adopting the above technical solution, the settings of the paper feeding mechanism 6 and the paper storage box 5 can realize batch printing or copying of test papers.

[0033] In this embodiment, a support rod 7 is fixed at the rear of the printer main body 1, and a display screen 8 is cooperatively installed at the upper end of the support rod 7; the display screen 8 is electrically connected to the image output module 93.

[0034] By adopting the above technical solution, when copying a test paper, after being scanned by the image scanning component 3, image information will be formed; after noise removal, the image information of the test paper will be displayed on the display screen 8, so as to facilitate the operator to view the noise removal effect; if the noise removal is not ideal, deep processing can be carried out.

[0035] In this embodiment, the image scanning component 3 has a light source, a scanner, a lens group and an image sensor. The light source is a light emitting diode or a cold cathode fluorescent lamp.

[0036] As a further description of this embodiment, the printer utilizes the potential characteristics of the photoconductor to charge the photoconductor in a state where it is not illuminated, so that its surface is uniformly charged, and then through the principle of optical imaging, the original image is imaged on the photoconductor. The part with the image is not illuminated, so the surface of the photoconductor still has charges, while the area without the image is illuminated, so the charges on the surface of the photoconductor disappear through the grounding of the substrate, thus forming an electrostatic latent image; then the electrostatic latent image is read by the image sensor; therefore, the paper test paper is converted into an electronic image for subsequent noise removal.

[0037] In this embodiment, a convolutional neural network (CNN) and a generative adversarial network (GAN) are established in the processor 91; among them, the convolutional neural network learns the feature representation of the image by training a deep convolutional neural network, so as to realize image denoising. The network usually consists of multiple convolutional layers, pooling layers and fully connected layers, and can automatically learn the features and noise patterns of the image from a large amount of training data. It can effectively remove various types of noise while better retaining the detail information of the image. For complex noise situations, deep learning methods usually can achieve better results than traditional methods.

[0038] The adversarial network consists of a generator and a discriminator. The generator is used to generate the denoised image, and the discriminator is used to determine whether the input image is a real noise-free image or an image generated by the generator. Through adversarial training, the generator continuously learns how to generate more realistic denoised images, while the discriminator continuously improves its discrimination ability; it can generate very realistic denoised images and also achieve good results for some noise situations that are difficult to handle by traditional methods.

[0039] Regarding the specific algorithm for noise in this embodiment

[0040] By analyzing the pixel value distribution, color information, texture features, etc. of the image, the noise areas in the image can be accurately identified. The specific method is as follows:

[0041] 1. Statistical analysis: Conduct statistical analysis on each pixel point and its neighborhood in the image, and calculate statistics such as the mean and variance of the pixel values. If the pixel value of a certain pixel point has a large difference from the pixel values of its neighborhood and exceeds the preset threshold range, then this pixel point is determined to be noise.

[0042] 2. Frequency analysis: Perform Fourier transform on the image to convert it from the spatial domain to the frequency domain. In the frequency domain, noise usually appears as high-frequency components. By analyzing the energy distribution in the frequency domain, the noise areas in the image can be determined.

[0043] Noise removal processing algorithm

[0044] For slightly noisy areas, the mean filtering method is adopted. Select a certain-sized neighborhood around the noisy pixel point, calculate the average value of the pixel values in the neighborhood, and replace the value of the noisy pixel point with this average value.

[0045] For example: Select a 3×3 neighborhood. For the central pixel point (x, y), the pixel values in its neighborhood are respectively: [a1, a2, a3], [a4, a(x, y), a5], [a6, a7, a8].

[0046] Then the new pixel value is (a1 + a2 + a3 + a4 + a(x,y) + a5 + a6 + a7 + a8) / 9.

[0047] For moderately noisy areas, the median filtering method is adopted. Similarly, select the neighborhood around the noisy pixel point, sort the pixel values in the neighborhood, and take the median value as the new value of the noisy pixel point. Median filtering can effectively remove impulse noise such as salt-and-pepper noise.

[0048] Similarly, select a 3×3 neighborhood, sort the pixel gray values in the neighborhood, and take the median value after sorting as the new gray value of the central pixel point; sort the 9 pixel values from small to large and take the middle value as the new value of the central pixel point.

[0049] For complex noise regions, an adaptive filtering method is adopted. This method automatically adjusts the parameters of the filter according to the local features of the image to adapt to different noise situations. For example, in the edge regions of the image, the parameters of the filter will be adjusted to pay more attention to preserving edge information and avoid edge blurring.

[0050] The Gaussian filtering algorithm is adopted to determine the weights of pixel points in the neighborhood according to the Gaussian distribution; the pixel weights near the central pixel point are larger, and the weights decrease as the distance from the center increases; the new value of the central pixel point is calculated by the method of weighted average.

[0051] Example: For a 3×3 neighborhood, the weights of each pixel can be calculated according to the two-dimensional Gaussian function. For example, the weight of the central pixel is 0.4, and the weights of the surrounding pixels decrease in turn; assuming that the pixel values in the neighborhood are the same as above, the new pixel value is a1*weight1 + a2*weight2 + … + a8*weight8 + a(x,y)*weight of the central pixel.

[0052] Detail preservation

[0053] Edge detection: Edge detection algorithms such as Sobel operator, Canny operator, etc. are adopted to detect the edges in the image. During the denoising process, special processing is performed on the pixel points in the edge regions to ensure the clarity and sharpness of the edges.

[0054] High-frequency enhancement: The image is processed by high-frequency enhancement to highlight the detail information of the image. By performing high-pass filtering on the image, the high-frequency components in the image are extracted, and then they are superimposed on the original image to enhance the details of the image.

[0055] The specific process for image noise removal is as follows:

[0056] I. Image input

[0057] First, receive the image that needs to be processed for noise removal. It can be the original image directly obtained from the camera or the image file read from the storage device.

[0058] II. Preliminary analysis

[0059] Visual inspection

[0060] Intuitively observe the image to judge whether there are obvious noises. For example, in the dark regions, near the high-contrast edges or in the large-area single-color regions, the noises may be more obvious.

[0061] If granular, speckled or random abnormal pixels can be observed by the naked eye, it is preliminarily judged that the image may have a noise problem.

[0062] Statistical analysis

[0063] Calculate some basic statistical parameters of the image, such as the mean, variance, standard deviation of pixel values, etc.

[0064] If the variance or standard deviation is large, it indicates that the pixel values of the image fluctuate greatly and there may be noise.

[0065] III. Noise Detection

[0066] Spatial Domain Detection

[0067] Traverse each pixel point of the image and check its difference from the surrounding pixel points.

[0068] If the gray value difference between a pixel point and the surrounding neighboring pixel points significantly exceeds a certain range, this pixel point can be considered as a possible noise point. For example, a small window centered on the current pixel can be set, and the mean and standard deviation of the pixels within the window are calculated. If the difference between the current pixel and the mean exceeds several times the standard deviation, it is judged as noise.

[0069] For color images, similar detections can be performed on each color channel separately.

[0070] Frequency Domain Detection

[0071] Perform a Fourier transform on the image to convert it from the spatial domain to the frequency domain.

[0072] In the frequency domain, noise usually appears as high-frequency components. Analyze the energy distribution in the frequency domain. If the energy in the high-frequency part is abnormally high, it may indicate that there is noise in the image.

[0073] IV. Noise Degree Evaluation

[0074] Quantify the number of noise points

[0075] According to the results of noise detection, count the number of pixel points determined to be noise points.

[0076] Calculate the proportion of noise pixel points in the total pixel points of the image to evaluate the severity of the noise.

[0077] Classify the types of noise

[0078] According to the characteristics of the noise, classify it into different types, such as Gaussian noise, salt-and-pepper noise, etc.

[0079] Different types of noise may require different removal methods, so accurately classifying the types of noise helps with subsequent processing.

[0080] V. Decision Making

[0081] Whether noise removal is needed

[0082] If the degree of noise is relatively low and does not affect the overall quality and readability of the image, it is possible to choose not to perform denoising processing to avoid unnecessary consumption of computing resources and possible loss of image details.

[0083] If the degree of noise is relatively high and significantly affects the image quality, then it is decided to perform denoising processing.

[0084] Select a denoising method

[0085] According to the type and severity of the noise, select a suitable denoising method. For example, for Gaussian noise, methods such as Gaussian filtering and bilateral filtering can be selected; for salt-and-pepper noise, methods such as median filtering can be selected.

[0086] Consider the characteristics of the image, such as the richness of texture, the sharpness of edges, etc., and select a method that can retain the image details to the greatest extent while removing the noise.

[0087] VI. Result verification

[0088] Visual inspection

[0089] Perform a visual inspection on the denoised image again to observe whether the noise is effectively removed and whether the overall quality of the image is improved.

[0090] Check whether the edges, details, and colors of the image remain natural without problems such as excessive blurring or distortion.

[0091] Quantitative evaluation

[0092] Calculate some statistical parameters of the denoised image, such as mean, variance, signal-to-noise ratio, etc., and compare them with the original image to evaluate the denoising effect.

[0093] Some image quality evaluation metrics can be used, such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), etc., to objectively measure the quality change of the image before and after denoising.

[0094] If the denoising effect is not satisfactory, the denoising parameters can be adjusted or other denoising methods can be tried, and the processing can be repeated until a satisfactory effect is achieved.

[0095] For those skilled in the art, it is obvious that the present utility model is not limited to the details of the above exemplary embodiments, and can be implemented in other specific forms without departing from the spirit or basic characteristics of the present utility model. Therefore, in any aspect, the embodiments should be regarded as exemplary and non-limiting. The scope of the present utility model is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be encompassed within the present utility model. Any reference signs in the claims should not be construed as limiting the claimed rights.

[0096] In addition, it should be understood that although this specification is described in terms of embodiments, not every embodiment only contains an independent technical solution. This narrative manner of the specification is only for clarity. Those skilled in the art should regard the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A test paper printer with a noise reduction function, characterized in that: including a printer main body (1), the top of the printer main body (1) is provided with an optical glass (2) for test paper copying, an image scanning component (3) capable of acquiring test paper content is arranged below the optical glass (2), and a cover plate (4) is movably connected to the top of the printer main body (1); a control system (9) is arranged inside the printer main body (1); the control system (9) has an image acquisition module (92) and an image output module (93), both the image acquisition module (92) and the image output module (93) are electrically connected to a processor (91); the processor (91) is also electrically connected to a control circuit (94); the image scanning component (3) is electrically connected to the processor (91); a support rod (7) is fixed at the rear of the printer main body (1), and a display screen (8) is fitted and installed at the upper end of the support rod (7); the display screen (8) is electrically connected to the image output module (93); the image scanning component (3) has a light source, a scanner, a lens group and an image sensor; the light source is a light emitting diode or a cold cathode fluorescent lamp.

2. The test paper printer with a noise removal function according to claim 1, characterized in that: a paper feeding mechanism (6) is fitted and installed at the front end of the printer main body (1), and a paper storage box (5) is arranged outside the paper feeding mechanism (6).

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